Optimizing virtual machine scheduling in numa multicore systems

An increasing number of new multicore systems use the Non-Uniform Memory Access architecture due to its scalable memory performance. However, the complex interplay among data locality, contention on shared on-chip memory resources, and cross-node data sharing overhead, makes the delivery of an optimal and predictable program performance difficult. Vir-tualization further complicates the scheduling problem. Due to abstract and inaccurate mappings from virtual hardware to machine hardware, program and system-level optimizations are often not effective within virtual machines. We find that the penalty to access the “uncore ” memory subsystem is an effective metric to predict program perfor-mance in NUMA multicore systems. Based on this metric, we add NUMA awareness to the virtual machine scheduling. We propose a Bias Random vCPU Migration (BRM) algorithm that dynamically migrates vCPUs to minimize the system-wide uncore penalty. We have implemented the scheme in the Xen virtual machine monitor. Experiment results on a two-way Intel NUMA multicore system with various workloads show that BRM is able to improve application performance by up to 31.7 % compared with the default Xen credit scheduler. More-over, BRM achieves predictable performance with, on average, no more than 2 % runtime variations. 1